Human Mammary Epithelial Cell Response to EGF, microarray analysis
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE15668 | GEO | 2010/04/14
SECONDARY ACCESSION(S): PRJNA115617
REPOSITORIES: GEO
Similar Datasets
Project description:To understand how integration of multiple data types can help decipher cellular responses at the systems level, we analyzed the mitogenic response of human mammary epithelial cells to epidermal growth factor (EGF) using whole genome microarrays, mass spectrometry-based proteomics and large-scale western blots with over 1000 antibodies. A time course analysis revealed significant differences in the expression of 3172 genes and 596 proteins, including protein phosphorylation changes measured by western blot. Integration of these disparate data types showed that each contributed qualitatively different components to the observed cell response to EGF and that varying degrees of concordance in gene expression and protein abundance measurements could be linked to specific biological processes. Networks inferred from individual data types were relatively limited, whereas networks derived from the integrated data recapitulated the known major cellular responses to EGF and exhibited more highly connected signaling nodes than networks derived from any individual dataset. While cell cycle regulatory pathways were altered as anticipated, we found the most robust response to mitogenic concentrations of EGF was induction of matrix metalloprotease cascades, highlighting the importance of the EGFR system as a regulator of the extracellular environment. These results demonstrate the value of integrating multiple levels of biological information to more accurately reconstruct networks of cellular response. Keywords: time-course analysis Synchronized human mammary epithelial cells were stimulated into mitosis using epidermal growth factor and samples were harvested at 1, 4, 8, 13, 18 and 24hrs for parallel analysis by microarray, global proteomics and western blot analysis.
2010-04-14 | E-GEOD-15668 | biostudies-arrayexpress
Project description:Brown2004 - NGF and EGF signaling
This model is described in the article:
The statistical mechanics of
complex signaling networks: nerve growth factor signaling.
Brown KS, Hill CC, Calero GA, Myers
CR, Lee KH, Sethna JP, Cerione RA.
Phys Biol 2004 Dec; 1(3-4):
184-195
Abstract:
The inherent complexity of cellular signaling networks and
their importance to a wide range of cellular functions
necessitates the development of modeling methods that can be
applied toward making predictions and highlighting the
appropriate experiments to test our understanding of how these
systems are designed and function. We use methods of
statistical mechanics to extract useful predictions for complex
cellular signaling networks. A key difficulty with signaling
models is that, while significant effort is being made to
experimentally measure the rate constants for individual steps
in these networks, many of the parameters required to describe
their behavior remain unknown or at best represent estimates.
To establish the usefulness of our approach, we have applied
our methods toward modeling the nerve growth factor
(NGF)-induced differentiation of neuronal cells. In particular,
we study the actions of NGF and mitogenic epidermal growth
factor (EGF) in rat pheochromocytoma (PC12) cells. Through a
network of intermediate signaling proteins, each of these
growth factors stimulates extracellular regulated kinase (Erk)
phosphorylation with distinct dynamical profiles. Using our
modeling approach, we are able to predict the influence of
specific signaling modules in determining the integrated
cellular response to the two growth factors. Our methods also
raise some interesting insights into the design and possible
evolution of cellular systems, highlighting an inherent
property of these systems that we call 'sloppiness.'
The figures in the paper show results from computations
performed over an ensemble of all parameter sets that fit the
available data. This file contains only the best fit parameters.
The full ensemble of parameters is available at
http://www.lassp.cornell.edu/sethna/GeneDynamics/PC12DataFiles/
(Also, the best-fit parameter set produces a curve for DN Rap1 that
is less "peakish" than the ensemble average.)
The conversion factors for EGF and NGF concentrations account
for their molecular weights and the density of cells in the culture
dish. These concentrations are saturating, so the exact values are
not critical.
Because the Erk data fit to measure only fold changes in
activity, there is no absolute scale for the y-axes. Thus the
curves from this file have different magnitudes than those
published.
To reproduce the figures from the paper:
2a) For EGF stimulation, set the initial concentration of EGF
to 100 ng/ml * 100020 (molecule/cell)/(ng/ml) = 10002000.
For NGF stimulation, set the initial concentration of NGF to
50 ng/ml * 4560 (molecule/cell)/(ng/ml) = 456000
5a) To simulate LY294002 addition, set kPI3KRas and kPI3K to
0.
5b) To simulate a dominant negative Rap1, set kRap1ToBRaf to
0.
To simulate a dominant negative Ras, set kRasToRaf1 and
kPI3KRas to 0.
Almost all the data fit with this model by the authors are
from Western blots. Given the uncertainties in antibody
effectiveness and other factors, one can't a priori derive a
conversion between the arbitrary units for a given set of data and
molecules per cell. So the authors used an adjustable "scale
factor" that converts between molecules per cell and Western blot
units.
For the EGF stimulation data in figure 2a) the scale factor
conversion is 1.414e-05 (U/mg)/(molecule/cell). For the NGF
stimulation data in figure 2a) it is 7.135e-06
(U/mg)/(molecule/cell).
This model is hosted on
BioModels Database
and identified by:
BIOMD0000000033.
To cite BioModels Database, please use:
BioModels Database:
An enhanced, curated and annotated resource for published
quantitative kinetic models.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
2024-09-02 | BIOMD0000000033 | BioModels
Project description:Epidermal growth factor (EGF) is a key regulatory growth factor activating a myriad of processes affecting cell proliferation and survival that are relevant to normal development and disease. Here we have used a combined approach to study the EGF dependent transcriptome of HeLa cells. We obtained mRNA expression profiles using multiple long oligonucleotide based microarray platforms (from Agilent, Operon, Febit, and Illumina) in combination with digital gene expression profiling (DGE) with the Illumina Genome Analyzer I (GA-I). By applying a procedure for cross-platform data meta-analysis based on rank product and global ancova tests, we establish a well validated gene set with transcript levels altered after EGF treatment. We used this robust gene list to build higher order networks of gene interaction by interconnecting associated networks, supporting and extending the important role of the EGF signaling pathway in cancer. In addition, we found a whole new set of genes previously unrelated to the currently accepted EGF associated cellular functions, among which are metallothionein genes. We propose the use of global genomic cross-validation to generate more reliable datasets derived from high content technologies (microarrays or deep sequencing). This approach should help to improve the confidence of downstream in silico functional inference analyses based on high content data. Keywords: treated vs. untreated comparison, time course Time course experiment comparing HeLa gene expression in response to EGF analyzed on different microarray platforms (Agilent, IMPPC, Illumina, and Operon) and by digital gene expression using short read high throughput tag sequencing. Three independent experiments were performed where HeLa cells were serum deprived for 24 hours and were either left untreated or treated with EGF for 6, and 24 h and harvested for RNA extraction. Technical dye swap duplicates were performed for each of the three biological replicates in both time points. Comparative genomic hybridization of HeLa cell genomic DNA versus poooled genomic DNA from blood obtained from human females conducted on commercial oligonucleotide microarrays (Human Genome CGH Microarray Kit 244A, Agilent Technologies) in order to assess DNA dosage dependence of gene expression levels and response to EGF. Digital gene expression using short read high throughput tag sequencing data submitted to NCBI's SRA
2011-07-18 | E-GEOD-17403 | biostudies-arrayexpress
Project description:described in: Systems-level interactions between insulin-EGF networks amplify mitogenic signaling.
Borisov N, Aksamitiene E, Kiyatkin A, Legewie S, Berkhout J, Maiwald T, Kaimachnikov NP, Timmer J, Hoek JB, Kholodenko BN.;Mol Syst Biol. 2009;5:256. Epub 2009 Apr 7. PMID:19357636; doi:10.1038/msb.2009.19
Abstract:
Crosstalk mechanisms have not been studied as thoroughly as individual signaling pathways. We exploit experimental and computational approaches to reveal how a concordant interplay between the insulin and epidermal growth factor (EGF) signaling networks can potentiate mitogenic signaling. In HEK293 cells, insulin is a poor activator of the Ras/ERK (extracellular signal-regulated kinase) cascade, yet it enhances ERK activation by low EGF doses. We find that major crosstalk mechanisms that amplify ERK signaling are localized upstream of Ras and at the Ras/Raf level. Computational modeling unveils how critical network nodes, the adaptor proteins GAB1 and insulin receptor substrate (IRS), Src kinase, and phosphatase SHP2, convert insulin-induced increase in the phosphatidylinositol-3,4,5-triphosphate (PIP(3)) concentration into enhanced Ras/ERK activity. The model predicts and experiments confirm that insulin-induced amplification of mitogenic signaling is abolished by disrupting PIP(3)-mediated positive feedback via GAB1 and IRS. We demonstrate that GAB1 behaves as a non-linear amplifier of mitogenic responses and insulin endows EGF signaling with robustness to GAB1 suppression. Our results show the feasibility of using computational models to identify key target combinations and predict complex cellular responses to a mixture of external cues.
An extracellular compartment with 34 times the volume of the cell was added and the association rate as well as the dissociation constants for Insulin and EGF binding were altered (kon'=34*kon, KD'=KD/34). This was done to allow using the concentrations for those species given in the article and retaining the same dynamics and Ligand depletion as in the matlab file the SBML file was exported from.
SBML model exported from PottersWheel on 2008-10-14 16:26:44.
This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.
2024-09-02 | BIOMD0000000223 | BioModels
Project description:Angiogenesis is defined as the formation of new capillaries by sprouting from preexisting vessels. It is mainly triggered by vascular endothelial growth factor (VEGF) and occurs in the adult primarily in wound healing processes or in pathologic tumor vessel growth. To identify genes specifically triggered by VEGF and involved in the process of angiogenesis, we utilized Affymetrix microarrays hybridized with cRNA of human umbilical vein endothelial cells (HUVEC) stimulated with either the main trigger of angiogenesis, VEGF or a more general mitogenic growth factor, EGF. This was done under the assumption that, in comparison to EGF, VEGF would induce an additional set of genes not solely required for cell division but necessary for angiogenesis. The obtained data revealed that in HUVEC VEGF induces a large repertoire of genes, the majority of which are not or significantly less induced by EGF. Keywords: time course
2009-06-30 | GSE10778 | GEO
Project description:Epidermal growth factor (EGF) is a key regulatory growth factor activating a myriad of processes affecting cell proliferation and survival that are relevant to normal development and disease. Here we have used a combined approach to study the EGF dependent transcriptome of HeLa cells. We obtained mRNA expression profiles using multiple long oligonucleotide based microarray platforms (from Agilent, Operon, Febit, and Illumina) in combination with digital gene expression profiling (DGE) with the Illumina Genome Analyzer I (GA-I). By applying a procedure for cross-platform data meta-analysis based on rank product and global ancova tests, we establish a well validated gene set with transcript levels altered after EGF treatment. We used this robust gene list to build higher order networks of gene interaction by interconnecting associated networks, supporting and extending the important role of the EGF signaling pathway in cancer. In addition, we found a whole new set of genes previously unrelated to the currently accepted EGF associated cellular functions, among which are metallothionein genes. We propose the use of global genomic cross-validation to generate more reliable datasets derived from high content technologies (microarrays or deep sequencing). This approach should help to improve the confidence of downstream in silico functional inference analyses based on high content data. Keywords: treated vs. untreated comparison, time course
2011-07-18 | GSE17403 | GEO
Project description:We have used an integrative high content analysis approach to identify the specific miRNAs implicated in EGF signaling in HeLa cells as potential mediators of cancer mediated functions. We have used microarray and deep-sequencing technologies in order to obtain a global view of the EGF miRNA transcriptome with a robust experimental cross-validation. By applying a procedure based on Rankprod tests, we have delimited a solid set of EGF-regulated miRNAs. After validating regulated miRNAs by RT-qPCR, we have derived protein networks and biological functions from the predicted targets of the regulated miRNAs to gain insight into the potential role of miRNAs in EGF-treated cells. In addition, we have analyzed sequence heterogeneity due to editing relative to the reference sequence (isomirs) among regulated miRNAs. Time course experiment comparing HeLa gene expression in response to EGF analyzed on different microarray platforms and by digital gene expression using short read high throughput sequencing by synthesis. Three independent experiments were performed where HeLa cells were serum deprived for 24 hours and were either left untreated or treated with EGF for 6 h and harvested for RNA extraction. treated vs. untreated comparison, time course
2013-06-11 | E-GEOD-41181 | biostudies-arrayexpress
Project description:We have used an integrative high content analysis approach to identify the specific miRNAs implicated in EGF signaling in HeLa cells as potential mediators of cancer mediated functions. We have used microarray and deep-sequencing technologies in order to obtain a global view of the EGF miRNA transcriptome with a robust experimental cross-validation. By applying a procedure based on Rankprod tests, we have delimited a solid set of EGF-regulated miRNAs. After validating regulated miRNAs by RT-qPCR, we have derived protein networks and biological functions from the predicted targets of the regulated miRNAs to gain insight into the potential role of miRNAs in EGF-treated cells. In addition, we have analyzed sequence heterogeneity due to editing relative to the reference sequence (isomirs) among regulated miRNAs. Time course experiment comparing HeLa gene expression in response to EGF analyzed on different microarray platforms and by digital gene expression using short read high throughput sequencing by synthesis. Three independent experiments were performed where HeLa cells were serum deprived for 24 hours and were either left untreated or treated with EGF for 6 h and harvested for RNA extraction. treated vs. untreated comparison, time course
2013-06-11 | E-GEOD-41225 | biostudies-arrayexpress
Project description:We have used an integrative high content analysis approach to identify the specific miRNAs implicated in EGF signaling in HeLa cells as potential mediators of cancer mediated functions. We have used microarray and deep-sequencing technologies in order to obtain a global view of the EGF miRNA transcriptome with a robust experimental cross-validation. By applying a procedure based on Rankprod tests, we have delimited a solid set of EGF-regulated miRNAs. After validating regulated miRNAs by RT-qPCR, we have derived protein networks and biological functions from the predicted targets of the regulated miRNAs to gain insight into the potential role of miRNAs in EGF-treated cells. In addition, we have analyzed sequence heterogeneity due to editing relative to the reference sequence (isomirs) among regulated miRNAs. Time course experiment comparing HeLa gene expression in response to EGF analyzed by small RNA-seq using Illumina 36-bp read massively parallel sequencing. Three independent experiments were performed where HeLa cells were serum deprived for 24 hours and were either left untreated or treated with EGF for 6h and harvested for RNA extraction. Thus, a total of 6 samples were analyzed, 3 controls and the 3 respective treated counterparts. These same samples were also analyzed in parallel on two different microarray platforms.
2013-06-11 | E-GEOD-41359 | biostudies-arrayexpress
Project description:Cells rely on input from extracellular growth factors to control their proliferation during development and adult homeostasis. Such mitogenic inputs are transmitted through multiple signaling pathways that synergize to precisely regulate cell cycle entry and progression. While the architecture of these signaling networks has been characterized in molecular detail, their relative contribution especially at later cell cycle stages remain largely unexplored. By combining quantitative time-resolved measurements of fluorescent reporters in untransformed human cells with targeted pharmacological inhibitors and statistical analysis, we quantified EGF-induced signal processing in individual cells over time and decomposed the dynamic contribution of downstream pathways. We define signaling features that encode information about extracellular ligand concentrations and critical time windows for inducing cell cycle transitions. We show that both ERK and PI3K activity is necessary for initial cell cycle entry, while only PI3K affect the duration of S-phase at later stages of mitogenic signaling.
2020-03-21 | GSE147259 | GEO